Papers by Shuhan Sun
A Self-verified Method for Exploring Simile Knowledge from Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) have succeeded in natural language processing because they learn generic knowledge from a large corpus. |
| Approach: | They propose a method that allows pre-trained language models to explore simile knowledge from PLMs . they enhance PLM models with a multi-level simile recognition task that evaluates similes aplenty . |
| Outcome: | The proposed method can explore more accurate simile knowledge for PLMs. |
Investigating Value-Reasoning Reliability in Small Large Language Models (2025.emnlp-main)
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| Challenge: | sLLMs have been widely deployed in practical applications, but little attention has been paid to their value-reasoning abilities, particularly in terms of reasoning reliability. |
| Approach: | They propose a systematic evaluation framework for assessing the Value-Reasoning Reliability of small Large Language models (sLLMs) . framework includes three core tasks: Repetition Consistency task, Interaction Stability task, and Open-ended Expression Consistencies task. |
| Outcome: | The proposed framework incorporates self-reported confidence scores to evaluate the model’s value reasoning reliability from two perspectives: the model's self awareness of its values, and its value-based decision-making. |
Efficient Knowledge Infusion via KG-LLM Alignment (2024.findings-acl)
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| Challenge: | Existing methods for knowledge infusion face knowledge mismatch and poor information compliance of LLMs with knowledge graphs. |
| Approach: | They propose a three-stage alignment strategy to enhance the LLM's capability to utilize information from knowledge graphs. |
| Outcome: | The proposed method outperforms baselines on biomedical question-answering datasets and outperformed existing methods. |
I run as fast as a rabbit, can you? A Multilingual Simile Dialogues Datasets (2023.findings-acl)
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| Challenge: | A simile is a figure of speech that compares two different things via shared properties. |
| Approach: | They propose a multilingual simile dialogue dataset that can be used to study similes in real-life scenarios. |
| Outcome: | The proposed dataset is the largest manually annotated simile dataset and contains both English and Chinese data. |